Thermally derived evapotranspiration from the Surface Temperature Initiated Closure (STIC) model improves cropland GPP estimates under dry conditions

Satellite-based gross primary productivity (GPP) monitoring in croplands is challenging due to our limited ability to empirically constrain photosynthetic capacity and associated parameters. Here, we investigated if integrating land surface temperature (T-R)-based evapotranspiration (ET) or latent heat flux (lambda E) into a Remote sensing-driven approach to Coupling Ecosystem Evapotranspiration and Photosynthesis (RCEEP) model, which is modified from the underlying water use efficiency method, can better characterize GPP under dry conditions as compared to the light use efficiency (LUE)-based models. We developed the new GPP model, termed STIC-RCEEP, by combining an ET model, called STIC (Surface Temperature Initiated Closure), and RCEEP. We compared the performance of STIC-RCEEP against four conventional LUE-based models (Vegetation Photosynthesis Model, MOD17, STIC-MOD17, STIC-LUE), using tower-based daily GPP data from different cropland flux sites across the globe. An evaluation of the five GPP models, all optimized using available 170 site-years data from the 22 flux sites, revealed the relatively better performance of the STIC-RCEEP (R-2 = 0.78 and RMSE = 2.5 gC m(-2) d(-1)) with respect to the other four LUE models. Error analysis revealed substantially less error of STICRCEEP particularly under the dry conditions and RMSE of STIC-RCEEP was 9%-14% less than the other models. This finding highlights the enhanced ability of thermal sensors to capture the water stress signals in ET and GPP, which is also evidenced by the substantially better performance of STIC than another widely-regarded nonthermal ET model (the Priestly Taylor - Jet Propulsion Laboratory model), under dry conditions. The improved GPP estimates from STIC-RCEEP under water-stressed environment opens avenues for further research and applications using existing and new TR-based sensors in coupled crop water use-productivity modeling.